KDTree
KDTree for fast generalized N-point problems
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new KDTree(opts?: object): KDTree;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.X? | ArrayLike[] | n_samples is the number of points in the data set, and n_features is the dimension of the parameter space. Note: if X is a C-contiguous array of doubles then data will not be copied. Otherwise, an internal copy will be made. |
opts.leaf_size? | any | Number of points at which to switch to brute-force. Changing leaf_size will not affect the results of a query, but can significantly impact the speed of a query and the memory required to store the constructed tree. The amount of memory needed to store the tree scales as approximately n_samples / leaf_size. For a specified leaf\_size, a leaf node is guaranteed to satisfy leaf\_size <= n\_points <= 2 \* leaf\_size, except in the case that n\_samples < leaf\_size. Default Value 40 |
opts.metric? | string | Metric to use for distance computation. Default is “minkowski”, which results in the standard Euclidean distance when p = 2. kd_tree.valid_metrics gives a list of the metrics which are valid for KDTree. See the documentation of scipy.spatial.distance (opens in a new tab) and the metrics listed in distance\_metrics for more information. Default Value 'minkowski' |
Returns
Defined in: generated/neighbors/KDTree.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/neighbors/KDTree.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/neighbors/KDTree.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/neighbors/KDTree.ts:19 (opens in a new tab)
id
string
Defined in: generated/neighbors/KDTree.ts:16 (opens in a new tab)
opts
any
Defined in: generated/neighbors/KDTree.ts:17 (opens in a new tab)
Accessors
data
The training data
Signature
data(): Promise<any>;Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:487 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/neighbors/KDTree.ts:47 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/neighbors/KDTree.ts:51 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/neighbors/KDTree.ts:101 (opens in a new tab)
get_arrays()
Get data and node arrays.
Signature
get_arrays(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:118 (opens in a new tab)
get_n_calls()
Get number of calls.
Signature
get_n_calls(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:144 (opens in a new tab)
get_tree_stats()
Get tree status.
Signature
get_tree_stats(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:170 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/neighbors/KDTree.ts:60 (opens in a new tab)
kernel_density()
Compute the kernel density estimate at points X with the given kernel, using the distance metric specified at tree creation.
Signature
kernel_density(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | An array of points to query. Last dimension should match dimension of training data. |
opts.atol? | number | Specify the desired absolute tolerance of the result. If the true result is K\_true, then the returned result K\_ret satisfies abs(K\_true \- K\_ret) < atol + rtol \* K\_ret The default is zero (i.e. machine precision). Default Value 0 |
opts.breadth_first? | boolean | If true, use a breadth-first search. If false (default) use a depth-first search. Breadth-first is generally faster for compact kernels and/or high tolerances. Default Value false |
opts.h? | number | the bandwidth of the kernel |
opts.kernel? | string | specify the kernel to use. Options are - ‘gaussian’ - ‘tophat’ - ‘epanechnikov’ - ‘exponential’ - ‘linear’ - ‘cosine’ Default is kernel = ‘gaussian’ Default Value 'gaussian' |
opts.return_log? | boolean | Return the logarithm of the result. This can be more accurate than returning the result itself for narrow kernels. Default Value false |
opts.rtol? | number | Specify the desired relative tolerance of the result. If the true result is K\_true, then the returned result K\_ret satisfies abs(K\_true \- K\_ret) < atol + rtol \* K\_ret The default is 1e-8 (i.e. machine precision). Default Value 1e-8 |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:196 (opens in a new tab)
query()
query the tree for the k nearest neighbors
Signature
query(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | An array of points to query |
opts.breadth_first? | boolean | if true, then query the nodes in a breadth-first manner. Otherwise, query the nodes in a depth-first manner. Default Value false |
opts.dualtree? | boolean | if true, use the dual tree formalism for the query: a tree is built for the query points, and the pair of trees is used to efficiently search this space. This can lead to better performance as the number of points grows large. Default Value false |
opts.k? | number | The number of nearest neighbors to return Default Value 1 |
opts.return_distance? | boolean | if true, return a tuple (d, i) of distances and indices if false, return array i Default Value true |
opts.sort_results? | boolean | if true, then distances and indices of each point are sorted on return, so that the first column contains the closest points. Otherwise, neighbors are returned in an arbitrary order. Default Value true |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:275 (opens in a new tab)
query_radius()
query the tree for neighbors within a radius r
Signature
query_radius(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | An array of points to query |
opts.count_only? | boolean | if true, return only the count of points within distance r if false, return the indices of all points within distance r If return_distance==true, setting count_only=true will result in an error. Default Value false |
opts.r? | any | r can be a single value, or an array of values of shape x.shape[:-1] if different radii are desired for each point. |
opts.return_distance? | boolean | if true, return distances to neighbors of each point if false, return only neighbors Note that unlike the query() method, setting return_distance=true here adds to the computation time. Not all distances need to be calculated explicitly for return_distance=false. Results are not sorted by default: see sort\_results keyword. Default Value false |
opts.sort_results? | boolean | if true, the distances and indices will be sorted before being returned. If false, the results will not be sorted. If return_distance == false, setting sort_results = true will result in an error. Default Value false |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:349 (opens in a new tab)
reset_n_calls()
Reset number of calls to 0.
Signature
reset_n_calls(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/neighbors/KDTree.ts:414 (opens in a new tab)
two_point_correlation()
Compute the two-point correlation function
Signature
two_point_correlation(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | An array of points to query. Last dimension should match dimension of training data. |
opts.dualtree? | boolean | If true, use a dualtree algorithm. Otherwise, use a single-tree algorithm. Dual tree algorithms can have better scaling for large N. Default Value false |
opts.r? | ArrayLike | A one-dimensional array of distances |
Returns
Promise<ArrayLike>
Defined in: generated/neighbors/KDTree.ts:440 (opens in a new tab)